Official Google Cloud Certified Professional Data Engineer Study Guide. Dan Sullivan

Essentials Review Questions

      16  Chapter 9 Deploying Machine Learning Pipelines Structure of ML Pipelines GCP Options for Deploying Machine Learning Pipeline Exam Essentials Review Questions

      17  Chapter 10 Choosing Training and Serving Infrastructure Hardware Accelerators Distributed and Single Machine Infrastructure Edge Computing with GCP Exam Essentials Review Questions

      18  Chapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models Three Types of Machine Learning Algorithms Deep Learning Engineering Machine Learning Models Common Sources of Error in Machine Learning Models Exam Essentials Review Questions

      19  Chapter 12 Leveraging Prebuilt Models as a Service Sight Conversation Language Structured Data Exam Essentials Review Questions

      20  Appendix Answers to Review Questions Chapter 1: Selecting Appropriate Storage Technologies Chapter 2: Building and Operationalizing Storage Systems Chapter 3: Designing Data Pipelines Chapter 4: Designing a Data Processing Solution Chapter 5: Building and Operationalizing Processing Infrastructure Chapter 6: Designing for Security and Compliance Chapter 7: Designing Databases for Reliability, Scalability, and Availability Chapter 8: Understanding Data Operations for Flexibility and Portability Chapter 9: Deploying Machine Learning Pipelines Chapter 10: Choosing Training and Serving Infrastructure Chapter 11: Measuring, Monitoring, and Troubleshooting Machine Learning Models Chapter 12: Leveraging Prebuilt Models as a Service

      21  Index

      22  End User License Agreement

       List of Tables

      1 Chapter 1Table 1.1Table 1.2Table 1.3Table 1.4Table 1.5Table 1.6Table 1.7

      2 Chapter 9Table 9.1

      3 Chapter 11Table 11.1Table 11.2Table 11.3

      List of Illustrations

      1 Chapter 1Figure 1.1 Choosing a storage technology in GCPFigure 1.2 Example graph of friends

      2 Chapter 2Figure 2.1 Basic Cloud SQL configurationFigure 2.2 Optional configuration parameters in Cloud SQLFigure 2.3 Configuring Cloud SpannerFigure 2.4 Configuring a Bigtable clusterFigure 2.5 Cost of a three-node Bigtable production clusterFigure 2.6 BigQuery interactive interface with sample query

      3 Chapter 3Figure 3.1 A simple directed graphFigure 3.2 A simple cyclic graphFigure 3.3 An example ingestion stage of a data pipelineFigure 3.4 Data pipeline with transformationsFigure 3.5 Example pipeline DAG with storageFigure 3.6 Complete data pipeline from ingestion to analysisFigure 3.7 A stream with sliding and tumbling three windowFigure 3.8 Data pipeline with both a hot path and a cold pathFigure 3.9 Creating a Cloud Dataflow job in the console using a templateFigure 3.10 Specifying parameters for the Word Count Template

      4 Chapter 4Figure 4.1 Summary of compute option featuresFigure 4.2 Edge computing brings some computation outside the cloud and closer to where th...Figure 4.3 A hub-and-spoke message broker patternFigure 4.4 Simple asynchronous message processing

      5 Chapter 5Figure 5.1 A basic VM instance provisioning form in the cloud consoleFigure 5.2 Form for creating an instance templateFigure 5.3 Form for creating a managed instance groupFigure 5.4 Cloud console user interface for creating a Kubernetes clusterFigure 5.5 Form to create a Bigtable instanceFigure 5.6 A cloud console form for creating a Cloud Dataproc clusterFigure 5.7 A example log listing in Stackdriver Logging

      6 Chapter 6Figure 6.1 Create Role form in the cloud consoleFigure 6.2 Selecting permissions from predefined rolesFigure 6.3 An example of a redacted image generated by the Data Loss Prevention API

      7 Chapter 7Figure 7.1 Cloud Bigtable uses a cluster of VMs and the Colossus filesystem for storing, a...Figure 7.2 An example heatmap generated by Cloud Bigtable Key